Test Scenario Generation for Driving Simulators Using Constrained Randomization Technique

Paper #:
  • 2017-01-1672

Published:
  • 2017-03-28
DOI:
  • 10.4271/2017-01-1672
Citation:
Khastgir, S., Dhadyalla, G., Birrell, S., Redmond, S. et al., "Test Scenario Generation for Driving Simulators Using Constrained Randomization Technique," SAE Technical Paper 2017-01-1672, 2017, doi:10.4271/2017-01-1672.
Pages:
7
Abstract:
The advent of Advanced Driver Assistance Systems (ADAS) and automated driving has offered a new challenge for functional verification and validation. The explosion of the test sample space for possible combinations of inputs needs to be handled in an intelligent manner to meet cost and time targets for the development of such systems.This paper addresses this research gap by using constrained randomization techniques for the creation of the required test scenarios and test cases. Furthermore, this paper proposes an automated constrained randomized test scenario generation framework for testing of ADAS and automated systems in a driving simulator setup. The constrained randomization approach is deployed at two levels: 1) test scenario randomization 2) test case randomization. The novelty of the proposed approach is in applying the constrained randomization method to generate test scenarios and test cases for automotive system and system of systems in a driving simulator environment. Test scenario randomization is used to automatically create the base scenario w.r.t., possible path trajectories of vehicles, environment variables and traffic variables. Test case randomization is achieved by real-time communication between the driving simulator and the constrained randomization tool via a TCP/IP HiL interface (client-server interface). Constrained randomization is then used to intelligently explore the possible sample space to find the corner cases for which an ADAS or an automated system may fail. For the use case discussed in this paper, 150 test scenarios were created, with 20 test cases for each test scenario, i.e., 3000 test runs were executed which were generated using a constrained randomization method.The test setup comprises of the 3xD Simulator for Intelligent Vehicles at WMG, University of Warwick, UK, which has been integrated with the Vitaq® tool, which is a constrained randomization test automation tool developed by Vertizan Limited.
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